The Master of Management in Applied AI and Data-Driven Decision-Making (MAIDM) is a 20-month, part-time graduate program designed for early-to-mid career professionals who want to build expertise in AI and analytics without stepping away from their careers. Unlike technical programs that emphasize programming, this program focuses on the applied use of AI and analytics within a business management context. No programming background is required. Instead, students will develop the strategic skills needed to incorporate AI and data-driven insights into managerial decision-making and to effectively lead and manage teams of analysts.
Curriculum Highlights:
Graduates will develop a foundational understanding of applied AI and data-driven decision-making techniques for business and organizational contexts. They will be able to interpret AI-generated outputs to identify and communicate business value effectively, demonstrating leadership, communication and critical thinking skills. Additionally, they will formulate leadership strategies for managing data-driven and AI projects and teams, while critically evaluating the ethical, legal and regulatory considerations associated with these technologies. Graduates will also be equipped to assess and adapt to new developments in the application of applied AI and data-driven decision-making.
The program is comprised of 10 courses delivered over five terms (two per term) in a hybrid format, combining asynchronous online modules with three weekend on-campus residencies per term.
This course will provide an overview of the process involved in acquiring data from different sources and in different formats, and the resulting need for organizing data in a structured form for exploration and for developing insights. Students will then be taught visualization techniques for effective communication and storytelling. This course will incorporate data sources such as Google, MS Analytics tools such as Power BI and Excel, and visualization packages such as Tableau.
This course is designed to equip managers with the skills and knowledge to leverage predictive analytics in decision-making processes. Students will learn about different techniques to create models to forecast future trends, and than gain insights from data to support strategic decisions. Various techniques would be deployed on both real and simulated cases, which will be implemented, solved and analyzed in desktop packages such as Analytic Solver, SAS and MS Azure.
This course will provide an overview of the artificial intelligence (AI) landscape including discussion about basic machine learning (ML) concepts such as supervised and unsupervised learning, and more advanced concepts of AI. Several case studies (real/simulated) will be used to illustrate the applications of applied and generative AI algorithms to business problems, which will be further analyzed to gain insights and business value. Desktop packages including Analytic Solver, Mathematica and MS Analytics will be used in this course.
This course provides an in-depth exploration of the principles and practices involved in managing artificial intelligence and analytics projects. Students will learn how to effectively plan, execute, and oversee projects leveraging the power of AI and Analytics. This course will make use of Microsoft Project software, case studies and experiential learning to introduce and reinforce the managerial and technical skills of AI and data analytics driven investigation.
This course is designed to equip managers with the skills and knowledge to leverage AI and analytics in strategic decision-making and organizational success. This course will discuss the significance of data in strategy development, fostering a culture around innovation and analytics, and leading/managing change. It will make use of case studies, role plays, case studies/group projects, and presentations to both learn and demonstrate how to lead and implement data driven organizational initiatives.
This course will introduce students to the basics of decision-making, incorporate the utility and preferences of decision makers and then consider uncertainty. Conceptual, diagrammatic and computer models would be built in this regard. Both real and simulated case studies depicting managerial problems from different functional areas of business will be used in the course, and a wide range of industries will be modeled and analyzed in MS Excel and Arena.
This course is designed to provide managers with the tools and techniques to optimize business processes and decision-making through analytics and will focus on how to help managers understand and apply optimization techniques to business problems. A variety of business problems from different areas will be introduced, modeled and solved in a desktop environment, and the resulting solutions analyzed to gain managerial insights. Both real and simulated case studies will be used in the course, and the computer implementation will be in Analytic Solver, which integrates into the MS Excel environment and thus suitable for such a course.
This course will provide a comprehensive overview of the ethical, governance, and compliance issues associated with the development and deployment of artificial intelligence (AI) and analytics systems. It will examine case studies and relevant developments to explore the set of guiding principles that stakeholders (academics, government, intergovernmental entities, non-profit organizations, and private companies) use to ensure AI technology is developed and used responsibly, transparently, and in alignment with societal values.
Value creation is the process that coverts inputs into outputs that have more worth than their components. Value can be seen as how much customers will pay for a product or service compared to what a company spends in producing it. This course will use a series of case studies and examples from different functional areas of business and demonstrate how applied AI and analytics can help create value, and touch on the importance of a multi-disciplinary perspective to business problems.
This course will focus on the process of tracking and analyzing data related to marketing activities, which in turn can enable organizations to improve their customer experiences, increase the return on investment (ROI) of marketing efforts, and craft future marketing strategies. The course will cover the three spheres of marketing analytics: analyzing the present; reporting on the past; and predicting the future. Real/Simulated data and case studies will be used to cover the spheres, and the discussions will be supported by outputs from desktop and computer programs.
Our DeGroote team is here to support you every step of the way. If you have any questions or need help with your application, don’t hesitate to reach out.
